Intention to use big data technology in teaching among higher education educators in Yunnan, China - Wang, Qianhui (2022)
Big data technology has brought a huge impact to the education field. Personalized learning analysis and intelligent decision support based on accurate learning diagnoses made possible with big data have greatly improved the quality of education, optimized educational management, and become vital support for realizing education modernization. However, there are still problems in teachers’ use of big data technology in teaching practice, and there is a lack of in-depth theoretical analysis and practical research on exploring the factors influencing teachers’ behavioural intentions to use big data technology in teaching. Therefore, the purpose of this study is to explore the factors influencing behavioural intention to use big data technologies in teaching among higher education educators in Yunnan based mainly on the UTAUT model and to investigate the main challenges currently perceived by higher education teachers in the field of big data in education. This quantitative correlational study involved 193 higher education educators in Kunming, Yunnan Province, selected through simple random sampling. The survey questionnaire used in this study was adapted from past related studies. Its content validity was evaluated and confirmed by experts, and it was shown to have good structural validity through exploratory and confirmatory factor analyses. The reliability values of the instrument ranged from 0.887 to 0.904. The study used IBM SPSS version 25.0 to analyse the data. The results of the independent samples t-test showed that there were no significant differences in the behavioural intention of higher education educators to adopt big data technology in teaching based on gender. The results of the one-way ANOVA showed that there were no significant differences in the behavioural intentions of educators to adopt big data technology in teaching based on both age and teaching experience. In the correlation analysis, except for effort expectation (r (193) =0.134, p=0.063 >0.05), which was not significantly related to intention to use big data technology in teaching, performance expectation, social influence, and facilitating conditions all had significant relationships with the intention to use. The results of the multiple linear regression indicated that the combination of the four independent variables contributed significantly (39%; R2 = 0.357) to educators’ intention to use big data technology, which implies that the four studied variables predicted the dependent variable. Performance expectancy (β = 0.415, p = 0.000) was found to be the most significant factor contributing to educators’ intention to use big data technology. Therefore, universities should focus on increasing educators’ awareness of the benefits as well as the outcomes of using big data technologies in teaching to encourage more educators to adopt big data technology in teaching. However, it is noteworthy that after controlling for other variables, effort expectancy and social influence became insignificant predictors. In addition, the survey revealed that the biggest obstacle to big data in education is the expensive cost. In summary, this study contributes to the knowledge of big data technology and has major implications for educators’ practice of big data technology. On the one hand, the predictive model obtained from this study is likely to be useful as a reference for future research in related fields. On the other hand, based on the factors that influence educators’ intentions to use big data technology in teaching and the main barriers that big data mainly faces in the field of education, the study provides recommendations for university administrators and policy makers to motivate educators’ intentions to use big data technology in teaching so that intentions can eventually be turned into actual usage behaviours.